我收到一个数据集,其中包括先前转换为一个热编码的列。我想检索它的旧形状来做一些预处理和填充NAs方法,当然还有读取数据集的统计模型。
我得到的数据列:
<表类>
team2_offensive_derived_var_0
team2_offensive_derived_var_1
team2_offensive_derived_var_2
team2_offensive_derived_var_3
team2_offensive_derived_var_4
team2_offensive_derived_var_5
team2_offensive_derived_var_6
team2_offensive_derived_var_7
team2_offensive_derived_var_8
team2_offensive_derived_var_9
team2_offensive_derived_var_10
tbody><<tr>0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 表类>
您可以使用stack
:
cols = ['row_id', 'team2_offensive_derived']
out = df.replace(0, pd.NA).stack().rename_axis(cols).reset_index()[cols]
输出:
row_id team2_offensive_derived
0 0 team2_offensive_derived_var_10
1 1 team2_offensive_derived_var_9
2 2 team2_offensive_derived_var_8
3 3 team2_offensive_derived_var_7
4 4 team2_offensive_derived_var_6
5 5 team2_offensive_derived_var_5
6 6 team2_offensive_derived_var_4
7 7 team2_offensive_derived_var_3
8 8 team2_offensive_derived_var_2
9 9 team2_offensive_derived_var_1
10 10 team2_offensive_derived_var_0
有不同的列名:
out = (df
.replace(0, pd.NA)
.rename(columns=lambda x: x.replace('team2_offensive_derived_', ''))
.stack()
.rename_axis(cols)
.reset_index()[cols]
)
输出:
row_id team2_offensive_derived
0 0 var_10
1 1 var_9
2 2 var_8
3 3 var_7
4 4 var_6
5 5 var_5
6 6 var_4
7 7 var_3
8 8 var_2
9 9 var_1
10 10 var_0
使用说明:
#split by last previous _
df.columns = df.columns.str.rsplit('_', 2, expand=True)
#replace 0 to NaNs, so reshape remove rows with 0
df = df.replace(0, np.nan).stack([1,2])
#join MultiIndex value
df.index = df.index.map(lambda x: f'{x[1]} {x[2]}')
#create DataFrame
df = df.index.to_frame(name='team2_offensive_derived',index=False)
print (df)
team2_offensive_derived
0 var 10
1 var 9
2 var 8
3 var 7
4 var 6
5 var 5
6 var 4
7 var 3
8 var 2
9 var 1
10 var 0